The Modares Journal of Electrical Engineering
Volume:12 Issue: 3, 2012

This paper considers the problem of stable limit cycles generating in a class of uncertain nonlinear systems which leads to stable oscillations in the system’s output.This is a wanted behavior in many practical engineering problems. For this purpose, first the equation of the desirable limit cycle is achieved according to shape, amplitude and frequency of the required output oscillations. Then, the nonlinear control law is designed such that the phase portrait of the closed-loop system includes this stable limit cycle. The design of controller is based on the Lyapunov stability theorem which is suitable for stability analysis of the positive limit sets (the stable limit cycle is a positive limit set for the nonlinear dynamicl system). The proposed robust controller consists of two parts: nominal control law and additional term which guarantees the robust performance and vanishing the effect of uncertain terms. Finally, to show the applicability of the proposed method, an inertia pendulum system (with parametric uncertainties in its dynamical equations) is considered and the robust output oscillations are achieved by creating the desirable limit cycle in the close-loop system.

In this study, a robust controller is designed for fuzzy network control systems (NCSs) using the static output feedback. Delay and data packet dropout affect on the stability of network control systems, and therefore, the asymptotic stability condition is established considering delay and data packet dropout. Delay is time-varying while the lower and upper bounds for delay is defined, and the number of data packet dropout is unknown. Data drift is also an important phenomena that may occur when data is transmitted from sensors to the controller and from the controller to actuators. This phenomenon is modeled as a stochastic variable with a probabilistic distribution. For stability analyses, Lyapunov–Krasovskii functions, which depend on the limits of delay and data packet dropout, are used. Results of controller design are derived as Linear Matrix Inequalities (LMIs). A numerical example is adopted to show the effectiveness of the proposed approach.

This paper presents a parameterization method to optimal trajectory planning and dynamic obstacle avoidance for Omni-directional robots. The aim of trajectory planning is minimizing a quadratic cost function while a maximum limitation on velocity and acceleration of robot is considered. First, we parameterize the trajectory using polynomial functions with unknown coefficients which transforms trajectory planning to an optimization problem. Then we use a novel method to solving the optimization problem and obtaining the unknown parameters. Finally, the efficiency of proposed approach is confirmed by simulation.

This paper presents a gain scheduled autopilot for pitch channel of a flying vehicle. The selected method is based on polynomial fuzzy systems. The method does not involve linearization about operating point. First the polynomial fuzzy model of pitch channel of the flight body is derived. Next, using polynomial fuzzy system methodology the controller is design such that the outputs of the nonlinear plant drive to follow those of a stable reference model. Because of avoiding actuator saturation, some constraints derived that guarantees the amplitude of control signals be less than a specific threshold. It is considered that the controller has a known structure like three-loop autopilot. In other words the three-loop fuzzy polynomial autopilot is design to satisfy stability and performance of the closed loop system over a wide range of parameter variation. Stability and performance conditions derived in terms of sum of square will solve numerically via SOSTOOLS.

In this paper, the problem of decentralized model reference adaptive control (MRAC) for a class of large scale systems with time varying delay in interconnected term and input and state delays is studied. To compensate the effect of input delay indirectly, a Smith predictor built on. To handle the effects of the time delays in input, the adaptive controller part includes two auxiliary dynamic filters with time varying gains. Under a usual assumption that the interconnections are assumed to be Lipschitz in its variables and uniformly in time with unknown Lipschitz gains, the difficulties from unknown interconnections are dealt. A generalized error is defined and by a suitable Lyapunov function, an adaptive controller is designed to stabilize it. Decentralized adaptive feedback controller can render the generalized error system uniformly ultimately bounded stable is designed. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed design techniques.

Considering that once-through Benson boiler is one of the most crucial equipment of a thermal power plant, occurrence of any fault in its different parts can lead to decrease of the performance of system, and even may cause system damage and endanger the human life. In this paper, due to the high complexity of the system's dynamic equations, we utilized data-based method for diagnosing the faults of the once-through Benson boiler. In order to enhance the fault diagnose (FD) system proficiency and also due to strong interactions between measurements, we decided to utilize six one-class support vector machine (SVM) algorithms to diagnose six major faults of once-through Benson boiler. In the proposed structure, each One-class SVM algorithm has been developed to diagnose one special fault. Finally, we carry out diverse test scenarios in different states of fault occurrence to evaluate the performance of the proposed FD system against the six major faults of the once-through Benson Boiler under conditions of noisy measurement.